How to Use the Elemeno MCP in LlamaIndex

Q: How do I filter out draft posts in LlamaIndex using Elemeno?

Your agent can call listpublishedcontent first. This ensures only live, published items are indexed into your knowledge base.

Index your Elemeno CMS content directly into vector stores using LlamaIndex for hallucination-free RAG.

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Connect Elemeno MCP to LlamaIndex

Create your Vinkius account to connect Elemeno to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Elemeno with LlamaIndex

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Build RAG pipelines with the Elemeno MCP Server

The `get_collection_item_details` tool retrieves the full content and metadata for specific items to populate your LlamaIndex vector store. Your pipeline queries this live data instead of relying on static, outdated local files. This MCP Server integration allows your agent to ground its responses in your actual, live CMS content. You get accurate answers based on real-time data, completely eliminating the typical hallucination issues of static models.

Index global pages and settings into your knowledge base

The `get_singleton_content` tool pulls unique global page data and settings directly into your LlamaIndex documents. Your agent reads these singletons to understand global site configurations or homepage layouts. By converting these singleton payloads into indexable nodes, LlamaIndex lets you run semantic searches across your global content. Your agent can instantly answer questions about your site's structural settings without manual data parsing.

Run semantic searches on live content collections

The `list_content_collections` tool maps out all configured collections so LlamaIndex knows which sources to index. Your agent can programmatically decide which collections to ingest based on user queries. After mapping the collections, the agent calls `list_collection_items` to pull the actual text blocks. This keeps your index updated with the latest published drafts without manual ingestion scripts.

Setup guide

Set up Elemeno MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Elemeno MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Elemeno tools.",
)
response = await agent.run("List recent Elemeno data")

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Common questions about Elemeno MCP in LlamaIndex

Yes. You convert the MCP tool outputs into LlamaIndex Document objects and index them in a vector store for semantic search.

Your agent can call `list_published_content` first. This ensures only live, published items are indexed into your knowledge base.

Yes. You can use the async tool list methods in LlamaIndex to fetch CMS data concurrently without blocking execution.

The agent uses pagination parameters. It loops through the collection list to ingest items in controlled batches.

Absolutely. Your content items and schema metadata are processed in an ephemeral, zero-trust V8 sandbox. No data persists on our MCP infrastructure, ensuring your proprietary CMS content remains secure.

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